54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
#!/usr/bin/env python
|
|
|
|
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
from concurrent.futures import ThreadPoolExecutor
|
|
from pathlib import Path
|
|
|
|
import numpy
|
|
import PIL
|
|
import torch
|
|
|
|
|
|
def concatenate_episodes(ep_dicts):
|
|
data_dict = {}
|
|
|
|
keys = ep_dicts[0].keys()
|
|
for key in keys:
|
|
if torch.is_tensor(ep_dicts[0][key][0]):
|
|
data_dict[key] = torch.cat([ep_dict[key] for ep_dict in ep_dicts])
|
|
else:
|
|
if key not in data_dict:
|
|
data_dict[key] = []
|
|
for ep_dict in ep_dicts:
|
|
for x in ep_dict[key]:
|
|
data_dict[key].append(x)
|
|
|
|
total_frames = data_dict["frame_index"].shape[0]
|
|
data_dict["index"] = torch.arange(0, total_frames, 1)
|
|
return data_dict
|
|
|
|
|
|
def save_images_concurrently(imgs_array: numpy.array, out_dir: Path, max_workers: int = 4):
|
|
out_dir = Path(out_dir)
|
|
out_dir.mkdir(parents=True, exist_ok=True)
|
|
|
|
def save_image(img_array, i, out_dir):
|
|
img = PIL.Image.fromarray(img_array)
|
|
img.save(str(out_dir / f"frame_{i:06d}.png"), quality=100)
|
|
|
|
num_images = len(imgs_array)
|
|
with ThreadPoolExecutor(max_workers=max_workers) as executor:
|
|
[executor.submit(save_image, imgs_array[i], i, out_dir) for i in range(num_images)]
|